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The Need for a Comprehensive Strategy to Evaluate Search Engine Performance in the Classroom
Given how ingrained Search Engines (SEs) are in educational environments, it is essential to evaluate their performance in response to inquiries that pertain to the classroom setting. In this position paper, we discuss the limitations of relying solely on traditional Information Retrieval metrics and usability studies… Read more
Recommended citation: Anuyah, O., Green, M., Milton, A., & Pera, M. S. (2019). "The Need for a Comprehensive Strategy to Evaluate Search Engine Performance in the Classroom " The 3rd KidRec Workshop co-located with ACM Interaction Design and Children (IDC) Conference. http://ashleemilton.github.io/files/comprehensiveKidRec2019.pdf
Here, There, and Everywhere: Building a Scaffolding for Children’s Learning Through Recommendations
Reading and literacy are on the decline among children. This is compounded by the fact that children have trouble with the discovery of resources that are appropriate, diverse, and appealing… Read more
Recommended citation: Milton, A., Murgia, E., Landoni, M., Huibers, T., & Pera, M. S. (2019). " Here, There, and Everywhere: Building a Scaffolding for Children’s Learning Through Recommendations " Proceddings of the 1st Workshop on the Impact of Recommender Systems with ACM RecSys 2019. http://ashleemilton.github.io/files/herethereImpact2019.pdf
StoryTime: eliciting preferences from children for book recommendations
We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences… Read more
Recommended citation: Milton, A., Green, M., Keener, A., Ames, J., Ekstrand, M. D., & Pera, M. S. (2019). "StoryTime: eliciting preferences from children for book recommendations " Proceedings of the 13th ACM Conference on Recommender Systems. 1(3). http://ashleemilton.github.io/files/storytimeRecSys2019.pdf
An empirical analysis of search engines’ response to web search queries associated with the classroom setting
The purpose of this paper is to examine strengths and limitations that search engines (SEs) exhibit when responding to web search queries associated with the grade school curriculum… Read more
Recommended citation: Anuyah, O., Milton, A., Green, M., & Pera, M. S. (2020). " An empirical analysis of search engines’ response to web search queries associated with the classroom setting " Aslib Journal of Information Management. 72(1). http://ashleemilton.github.io/files/empiricalAslib2020.pdf
Evaluating Information Retrieval Systems for Kids
Evaluation of information retrieval systems (IRS) is a prominent topic among information retrieval researchers–mainly directed at a general population. Children require unique IRS and by extension different ways to evaluate these systems, but as a large population that use IRS have largely been ignored on the evaluation front… Read more
Recommended citation: Milton, A. & Pera, M. S. (2020). "Evaluating Information Retrieval Systems for Kids " The 4th KidRec Workshop co-located with ACM Interaction Design and Children (IDC) Conference. http://ashleemilton.github.io/files/evaluatingKidRec2020.pdf
What Snippets Feel: Depression, Search, and Snippets
Mental health disorders (MHD) is a rising, yet stigmatized, topic in the United States. Individuals suffering from MHD are slowing starting to overcome this stigma by discussing how technology affects them. Researchers have explored behavioral nuances that emerge from interactions of individuals affected by MHD with persuasive technologies, mainly social media… Read more
Recommended citation: Milton, A. & Pera, M. S. (2020). "What Snippets Feel: Depression, Search, and Snippets " The 1st Joint Conference of the Information Retrieval Communities in Europe (CIRCLE). http://ashleemilton.github.io/files/feelCircle2020.pdf
“Don’t judge a book by its cover”: Exploring book traits children favor
We present the preliminary exploration we conducted to identify traits that can influence children’s preferences in books… Read more
Recommended citation: Milton, A., Batista, L., Allen, G., Gao, S., Ng, Y. K. D., & Pera, M. S. (2020). "“Don’t judge a book by its cover”: Exploring book traits children favor " Proceedings of the 14th ACM Conference on Recommender Systems. 1(3). http://ashleemilton.github.io/files/judgeRecSys2020.pdf
A Ranking Strategy to Promote Resources Supporting the Classroom Environment
Popular search engines (SE) favored by children are optimized neither to respond to their search behavior and abilities, nor retrieve resources that align with classroom standards. Further, attempts to adapt SE to support children’s inquiries have been one-dimensional, e.g., satisfy users’ reading skills or search expertise… Read more
Recommended citation: Milton, A., Anuya, O., Spear, L., Wright, K. L., & Pera, M. S. (2020). "A Ranking Strategy to Promote Resources Supporting the Classroom Environment " Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). 1(3). http://ashleemilton.github.io/files/rankingWIIAT2020.pdf
Pink for Princess, Blue for Superheroes: The Need to Examine Gender Sterotypes in Kid’s Products in Search and Recommendations
In this position paper, we argue for the need to investigate if and how gender stereotypes manifest in search and recommender systems… Read more
Recommended citation: Raj, A., Milton, A., & Ekstrand, M. D. (2021). "Pink for Princess, Blue for Superheroes: The Need to Examine Gender Sterotypes in Kid's Products in Search and Recommendations " Proceedings of the 5th KidRec Workshop co-located with ACM Interation, Design, and Children Conference. http://ashleemilton.github.io/files/pinkKidRec2021.pdf
To Infinity and Beyond! Accessibility is the Future for Kids’ Search Engines
Research in the area of search engines for children remains in its infancy. Seminal works have studied how children use mainstream search engines, as well as how to design and evaluate custom search engines explicitly for children… Read more
Recommended citation: Milton, A., Allen, G., & Pera, M. S. (2021). "To Infinity and Beyond! Accessibility is the Future for Kids Search Engines " IR for Children 2000-2020: Where Are We Now? Workshop co-located with the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. http://ashleemilton.github.io/files/infinityIR4C2021.pdf
Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior
Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online… Read more
Recommended citation: Spear, L., Milton, A., Allen, G., Raj, A., Green, M., Ekstrand, M. D., & Pera, M. S. (2021). "Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior " Proceedings of the 15th ACM Conference on Recommender Systems. http://ashleemilton.github.io/files/babysharkRecSys2021.pdf
Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters
Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the link between readability and IR is often overlooked. As a step towards advancing knowledge on the influence of readability on IR, we focus on Web search for children… Read more
Recommended citation: Allen, G., Milton, A., Wright, K. L., Fails, J. A., Kennington, C., & Pera, M. S. (2022). "Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters " The 44th European Conference on IR Research (ECIR). http://ashleemilton.github.io/files/readingECIR2022.pdf
The Users Aren’t Alright: Dangerous Mental Illness Behaviors and Recommendations
In this paper, we argue that recommendation systems are in a unique position to propagate dangerous and cruel behaviors to people with mental illnesses. Read more
Recommended citation: Milton, A. & Chancellor, S. (2022). " The Users Arent Alright: Dangerous Mental Illness Behaviors and Recommendations " Proceddings of the 5th Workshop FAccTRec Workshop: Responsible Recommendation with ACM RecSys 2022. http://ashleemilton.github.io/files/users2022facctrec.pdf
Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety
Researchers worldwide have explored the behavioral nuances that emerge from interactions of individuals afflicted by mental health disorders (MHD) with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to a persuasive technology that is part of their everyday lives web search engines (SE)… Read more
Recommended citation: Milton, A. & Pera, M. S. (2023). " Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety " ACM Transaction on the Web. http://ashleemilton.github.io/files/into2023tweb.pdf
I See Me Here: Mental Health Content, Community, and Algorithmic Curation on TikTok
Social media platforms are a place where people look for information and social support for mental health, resulting in both positive and negative effects on users. TikTok has gained notoriety for an abundance of mental health content and discourse… Read more
Recommended citation: Milton, A., Ajmani, L., DeVito, M.A. & Chancellor, S. (2023). " I See Me Here: Mental Health Content, Community, and Algorithmic Curation on TikTok " Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. http://ashleemilton.github.io/files/ISeeMeHere.pdf
Seeking in Cycles: How Users Leverage Personal Information Ecosystems to Find Mental Health Information
Information is crucial to how people understand their mental health and well-being, and many turn to online sources found through search engines and social media. We present an interview study… Read more
Recommended citation: Milton, A., Maestre, J.F., Roy, A., Umbach, R. & Chancellor, S. (2024). " Seeking in Cycles: How Users Leverage Personal Information Ecosystems to Find Mental Health Information " Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. http://ashleemilton.github.io/files/SeekinginCycles.pdf